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Chapter
Nov 9, 2020
Construction Research Congress 2020

CNN-Based Symbol Recognition in Piping Drawings

Publication: Construction Research Congress 2020: Computer Applications

ABSTRACT

Piping is an essential component in buildings, and its as-built information is critical to facility management tasks. Manually extracting piping information from legacy drawings that are in paper, PDF, or image format is mentally exerting, time-consuming, and error-prone. Symbol recognition is the core problem in the computer-based interpretation of piping drawings, and the main technical challenge is to determine robust features that are invariant to scaling, rotation, and translation. This thesis aims to use convolutional neural networks (CNNs) to automatically extract features from raw images, and consequently, to recognize symbols in piping drawings. In this thesis, the spatial transformer network (STN) is applied to improve the performance of a standard CNN model for recognizing piping symbols. For experimentation, eight types of symbols are synthesized based on the geometric constraints between the primitives. The experiment for symbol recognition is conducted, and the recognition accuracy of the CNN+STN model and the standard CNN model are compared. It is observed that the spatial transformer layer improves the accuracy in classifying piping symbols from 95.39% to 98.26%. Future works will focus on detecting symbols in piping drawings and collecting real drawings.

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Published In

Go to Construction Research Congress 2020
Construction Research Congress 2020: Computer Applications
Pages: 576 - 584
Editors: Pingbo Tang, Ph.D., Arizona State University, David Grau, Ph.D., Arizona State University, and Mounir El Asmar, Ph.D., Arizona State University
ISBN (Online): 978-0-7844-8286-5

History

Published online: Nov 9, 2020
Published in print: Nov 9, 2020

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Lyles School of Civil Engineering, Purdue Univ., West Lafayette, IN. E-mail: [email protected]
Jiannan Cai [email protected]
Lyles School of Civil Engineering, Purdue Univ., West Lafayette, IN. E-mail: [email protected]
Lyles School of Civil Engineering, Purdue Univ., West Lafayette, IN. E-mail: [email protected]

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